# Data Bias in Human Mobility is a Universal Phenomenon but is Highly Location-specific

arXiv: https://arxiv.org/abs/2508.00149 · HTML: https://mlsanity.com/paper/2508.00149/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation models](https://mlsanity.com/paper/2608.21567/) (2026)

## Abstract (arXiv)

Large-scale human mobility datasets play increasingly critical roles in many algorithmic systems, business processes and policy decisions. Unfortunately there has been little focus on understanding bias and other fundamental shortcomings of the datasets and how they impact downstream analyses and prediction tasks. In this work, we study `data production', quantifying not only whether individuals are represented in big digital datasets, but also how they are represented in terms of how much data they produce. We study GPS mobility data collected from anonymized smartphones for ten major US cities and find that data points can be more unequally distributed between users than wealth. We build models to predict the number of data points we can expect to be produced by the composition of demographic groups living in census tracts, and find strong effects of wealth, ethnicity, and education on data production. While we find that bias is a universal phenomenon, occurring in all cities, we further find that each city suffers from its own manifestation of it, and that location-specific models are required to model bias for each city. This work raises serious questions about general approaches to debias human mobility data and urges further research.